Published August 27, 2023 | Version 1
Dataset Open

FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees

  • 1. Norwegian Institute of Bioeconomy Research (NIBIO), Høgskoleveien 8, 1430 Ås Norway
  • 2. Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand
  • 3. Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Kamýcká 129, 165 00 Praha, Czech Republic
  • 4. School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, Australia
  • 5. TU Wien, Department of Geodesy and Geoinformation, E120-07, Wiedner Hauptstraße 8, 1040 Vienna, Austria


The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes.

In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. 

These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file.

A full description of the FOR-instance data can be found at 


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